A comprehensive model-assisted brain shift correction approach in image-guided neurosurgery: a case study in brain swelling and subsequent sag after craniotomy

Ma Luo; Sarah F. Frisken; Saramati Narasimhan; Logan W. Clements; Reid C. Thompson; Alexandra J. Golby; Michael I. Miga

doi:10.1117/12.2512763

8 March 2019 A comprehensive model-assisted brain shift correction approach in image-guided neurosurgery: a case study in brain swelling and subsequent sag after craniotomy

Ma Luo, Sarah F. Frisken, Saramati Narasimhan, Logan W. Clements, Reid C. Thompson, Alexandra J. Golby, Michael I. Miga

Author Affiliations +

Proceedings Volume 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling; 1095105 (2019) https://doi.org/10.1117/12.2512763
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Brain shift during neurosurgery can compromise the fidelity of image guidance and potentially lead to surgical error. We have developed a finite element model-based brain shift compensation strategy to correct preoperative images for improved intraoperative navigation. This workflow-friendly approach precomputes potential intraoperative deformations (a ‘deformation atlas’) via a biphasic-biomechanical-model accounting for brain deformation associated with cerebrospinal fluid drainage, osmotic agents, resection, and swelling. Intraoperatively, an inverse problem approach is employed to provide a combinatory fit from the atlas that best matches sparse intraoperative measurements. Subsequently, preoperative image is deformed accordingly to better reflect patient’s intraoperative anatomy. While we have performed several retrospective studies examining model’s accuracy using post- or intra-operative magnetic resonance imaging, one challenging task is to examine model’s ability to recapture shift due to the aforementioned effects independently with clinical data and in a longitudinal manner under varying conditions. The work here is a case study where swelling was observed at the initial stage of surgery (after craniotomy and dura opening), subsequently sag was observed in a later stage of resection. Intraoperative tissue swelling and sag were captured via an optically tracked stylus by identifying cortical surface vessel features (n = 9), and model-based correction was performed for these two distinct types of brain shift at different stages of the procedure. Within the course of the entire surgery, we estimate the cortical surface experienced a deformation trajectory absolute path length of approximately 19.4 ± 2.1 mm reflecting swelling followed by sag. Overall, model reduced swelling-induced shift from 7.3 ± 1.1 to 1.8 ± 0.5 mm (~74.6% correction); for subsequent sag movement, model reduced shift from 6.4 ± 1.5 to 1.4 ± 0.5 mm (~76.6% correction).

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Ma Luo, Sarah F. Frisken, Saramati Narasimhan, Logan W. Clements, Reid C. Thompson, Alexandra J. Golby, and Michael I. Miga "A comprehensive model-assisted brain shift correction approach in image-guided neurosurgery: a case study in brain swelling and subsequent sag after craniotomy", Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 1095105 (8 March 2019); https://doi.org/10.1117/12.2512763

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